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--- |
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pipeline_tag: translation |
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library_name: comet |
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language: |
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- multilingual |
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- af |
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- am |
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- ar |
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- as |
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- az |
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- be |
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- bg |
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- bn |
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- br |
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- bs |
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- ca |
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- cs |
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- cy |
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- da |
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- de |
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- el |
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- en |
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- eo |
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- es |
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- et |
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- eu |
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- fa |
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- fi |
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- fr |
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- fy |
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- ga |
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- gd |
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- gl |
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- gu |
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- ha |
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- he |
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- hi |
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- hr |
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- hu |
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- hy |
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- id |
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- is |
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- it |
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- ja |
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- jv |
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- ka |
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- kk |
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- km |
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- kn |
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- ko |
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- ku |
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- ky |
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- la |
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- lo |
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- lt |
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- lv |
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- mg |
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- mk |
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- ml |
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- mn |
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- mr |
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- ms |
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- my |
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- ne |
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- nl |
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- 'no' |
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- om |
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- or |
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- pa |
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- pl |
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- ps |
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- pt |
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- ro |
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- ru |
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- sa |
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- sd |
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- si |
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- sk |
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- sl |
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- so |
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- sq |
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- sr |
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- su |
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- sv |
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- sw |
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- ta |
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- te |
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- th |
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- tl |
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- tr |
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- ug |
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- uk |
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- ur |
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- uz |
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- vi |
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- xh |
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- yi |
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- zh |
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license: apache-2.0 |
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base_model: |
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- FacebookAI/xlm-roberta-large |
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--- |
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# COMET-poly-base-wmt25 |
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This model is based on [COMET-poly](https://github.com/zouharvi/COMET-poly), which is a fork but not compatible with original Unbabel's COMET. |
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To run the model, you need to first install this version of COMET either with: |
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```bash |
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pip install "git+https://github.com/zouharvi/COMET-poly#egg=comet-poly&subdirectory=comet_poly" |
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``` |
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or in editable mode: |
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```bash |
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git clone https://github.com/zouharvi/COMET-poly.git |
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cd COMET-poly |
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pip3 install -e comet_poly |
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``` |
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This model scores the translation `mt` given its source. It is a baseline model that other COMET-poly models are compared to. |
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```python |
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import comet_poly |
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model = comet_poly.load_from_checkpoint(comet_poly.download_model("zouharvi/COMET-poly-base-wmt25")) |
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data = [ |
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{ |
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"src": "Iceberg lettuce got its name in the 1920s when it was shipped packed in ice to stay fresh.", |
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"mt": "Eisbergsalat erhielt seinen Namen in den 1920er-Jahren, als er in Eis verpackt verschickt wurde, um frisch zu bleiben.", |
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}, |
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{ |
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"src": "Goats have rectangular pupils, which give them a wide field of vision—up to 320 degrees!", |
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"mt": "Kozy mají obdélníkové zornice, což jim umožňuje vidět skoro všude kolem sebe, aniž by musely otáčet hlavou.", |
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}, |
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{ |
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"src": "This helps them spot predators from almost all directions without moving their heads.", |
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"mt": "Điều này giúp chúng phát hiện kẻ săn mồi từ gần như mọi hướng mà không cần quay đầu.", |
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} |
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] |
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print("scores", model.predict(data, batch_size=8, gpus=1).scores) |
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``` |
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Outputs: |
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``` |
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scores [94.98790740966797, 77.56731414794922, 90.77655029296875] |
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``` |
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The training data is WMT up to 2024 (inclusive) with DA/ESA/MQM merged on a single scale. |
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This model is based on the work [TODO](TODO) which can be cited as: |
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``` |
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@misc{zuefle2025comet, |
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title={COMET-poly: Machine Translation Metric Grounded in Other Candidates}, |
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author={Maike Züfle, Vilém Zouhar, Tu Anh Dinh, Felipe Polo, Jan Niehues, Mrinmaya Sachan}, |
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year={2025}, |
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} |
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``` |
